Table of Contents
Advances in Artificial Neural Systems
Volume 2014, Article ID 595462, 10 pages
Research Article

Downscaling Statistical Model Techniques for Climate Change Analysis Applied to the Amazon Region

1Climate Science Program, Federal University of Rio Grande do Norte, 59082-200 Natal, RN, Brazil
2Instituto Nacional de Pesquisas Espaciais (INPE), Avenida dos Astronautas, 1.758 Jardim da Granja, 12227-010 São José dos Campos, SP, Brazil
3World Wild Life Fund Brazil (WWF), SHIS EQ QL 6/8 Conjunto, 71620-430 Brasilia, DF, Brazil

Received 8 November 2013; Revised 25 January 2014; Accepted 27 January 2014; Published 29 May 2014

Academic Editor: Ozgur Kisi

Copyright © 2014 David Mendes et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Belém and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fit.